Meta-SGD: The third algorithm we train our model on is Meta-SGD[1]}. This algorithm is same as MAML except for the fact that the learning rates \(\alpha \)  for each parameter in the learner is not a constant. Instead it is a trainable parameter that is also optimized by the meta-learner. The meta-learner therefore has control over not only the initialization, but also the learning rates of the parameters of the learner. In practice, this means that Meta-SGD shows faster convergence, higher accuracy and lower sensitivity to hyperparameters than MAML. The learner's training equation is same as that of MAML, except for \(\alpha \)  not being a constant. The meta-learner is trained according to the following modified equation
\((\theta ,\alpha ) = (\theta ,\alpha ) - \beta . \nabla _{(\theta ,\alpha )} \mathcal {L}_{test(\mathcal {T})}(\theta ^{\prime })\) 
